Ontology-based 3D pose estimation for autonomous object manipulation

In this paper a novel solution to the problem of guiding a robotic gripper in order to perform manipulation tasks, is presented. The proposed approach consists of two main modules corresponding to the training and testing sessions, respectively. During training, we employ an ontology-based framework with a view to the establishment of a database holding information regarding several geometrical attributes of the training objects. An accurate estimation of the 3D pose of an object-target is obtained during the testing phase and through the efficient exploitation of the established database. The most common solution to the 3D pose estimation problem implies extensive training sessions that are based on oversampled datasets containing several instances objects captured under varying view-points. However, such an approach engenders high complexity accompanied by large computational burden. We address this issue by proposing an ontology-based framework and a fuzzy-based approach that is able to efficiently interpolate between two known instances of the trained objects. Experimental results justify both our theoretical claims and our choice to adopt an ontology-based solution.